Increased Interferon Signaling in Vaginal Tissue of Patients With Primary Sjögren Syndrome
Bibliographic record
Abstract
Objective Vaginal dryness is an important factor influencing sexual function in women with primary Sjögren syndrome (pSS). Previous studies showed a higher degree of inflammation in vaginal biopsies from patients with pSS compared to non-pSS controls. However, the molecular pathways that drive this inflammation remain unclear. Therefore, the aim of this study was to investigate inflammatory pathway activity in the vaginal tissue of patients with pSS. Methods Vaginal biopsies of 8 premenopausal patients with pSS experiencing vaginal dryness and 7 age-matched non-pSS controls were included. Expression of genes involved in inflammation and tissue homeostasis was measured using NanoString technology and validated using TaqMan Real-Time PCR. Vaginal tissue sections were stained by immunohistochemistry for myxovirus resistance protein 1 (MxA) and CD123 (plasmacytoid dendritic cells [pDCs]). Results The most enriched pathway in vaginal biopsies from patients with pSS compared to non-pSS controls was the interferon (IFN) signaling pathway (P< 0.01). Pathway scores for Janus kinase and signal transducer and activator of transcription (JAK-STAT) and Notch signaling were also higher (P< 0.01 for both pathways). Conversely, transforming growth factor-β signaling and angiogenesis pathway scores were lower in pSS (P= 0.02 andP= 0.04, respectively). Differences in IFN signaling between patients with pSS and non-pSS controls were confirmed by PCR and MxA tissue staining. No CD123+ pDCs were detected in vaginal biopsies. IFN-stimulated gene expression levels correlated positively with CD45+ cell numbers in vaginal biopsies and serum anti-SSA/Ro positivity. Conclusion Upregulation of IFN signaling in vaginal tissue of women with pSS, along with its association with tissue pathology, suggests that IFNs contribute to inflammation of the vaginal wall and potentially also to clinical symptomatology (ie, vaginal dryness).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".